Golang中实现高效生成对抗网络算法的缓存机制。

package main

import (
    "fmt"
    "sync"
)

//定义一个存储键值对的map
var cache = make(map[string]interface{})

//定义一个缓存锁
var cacheLock sync.Mutex

//定义一个封装了缓存机制的函数
func cached(key string, getter func() interface{}) interface{} {
    cacheLock.Lock()
    defer cacheLock.Unlock()

    //检查缓存是否存在
    if value, ok := cache[key]; ok {
        return value
    }

    //如果不存在，则调用getter方法进行计算
    value := getter()

    //将计算结果存入缓存
    cache[key] = value

    return value
}

func main() {
    fmt.Println(cached("foo", func() interface{} {
        fmt.Println("Calculating foo.")
        return "bar"
    }))

    fmt.Println(cached("foo", func() interface{} {
        fmt.Println("Calculating foo.")
        return "baz"
    }))
}

package main

import (
    "fmt"
    "math/rand"
    "sync"
    "time"
)

const (
    realTotal    = 100000        //真实数据的总数
    fakeTotal    = 100000        //伪造数据的总数
    batchSize    = 100           //每个batch储存的数据量
    workerNumber = 10            //并发的worker数
    iteration    = 100           //迭代次数
    learningRate = 0.1           //学习速率
    cacheSize    = realTotal * 2 //缓存的空间大小
)

var (
    realData = make([]int, realTotal) //储存真实数据的数组
    fakeData = make([]int, fakeTotal) //储存伪造数据的数组
    cache    = make(map[string]interface{}, cacheSize)
    cacheLock sync.Mutex
)

func generate(i int) int {
    key := fmt.Sprintf("fake_%d", i/batchSize)
    return cached(key, func() interface{} {
        fmt.Printf("Calculating fake data [%d, %d).
", i, i+batchSize)
        output := make([]int, batchSize)
        //生成伪造数据
        for j := range output {
            output[j] = rand.Intn(realTotal)
        }
        return output
    }).([]int)[i%batchSize]
}

func cached(key string, getter func() interface{}) interface{} {
    cacheLock.Lock()
    defer cacheLock.Unlock()

    //先尝试从缓存中读取值
    if value, ok := cache[key]; ok {
        return value
    }

    //如果缓存中无值，则进行计算，并存入缓存中
    value := getter()
    cache[key] = value

    return value
}

func main() {
    rand.Seed(time.Now().Unix())
    //生成真实数据
    for i := 0; i < realTotal; i++ {
        realData[i] = rand.Intn(realTotal)
    }

    //初始化生成器和判别器的参数
    generatorParams := make([]float64, realTotal)
    for i := range generatorParams {
        generatorParams[i] = rand.Float64()
    }

    discriminatorParams := make([]float64, realTotal)
    for i := range discriminatorParams {
        discriminatorParams[i] = rand.Float64()
    }

    fmt.Println("Starting iterations.")
    //进行迭代更新
    for i := 0; i < iteration; i++ {
        //伪造数据的batch计数器
        fakeDataIndex := 0

        //使用worker进行并发处理
        var wg sync.WaitGroup
        for w := 0; w < workerNumber; w++ {
            wg.Add(1)

            //启动worker协程
            go func() {
                for j := 0; j < batchSize*2 && fakeDataIndex < fakeTotal; j++ {
                    if j < batchSize {
                        //使用生成器生成伪造数据
                        fakeData[fakeDataIndex] = generate(fakeDataIndex)
                    }

                    //使用判别器进行分类
                    var prob float64
                    if rand.Intn(2) == 0 {
                        //使用真实数据作为输入
                        prob = discriminatorParams[realData[rand.Intn(realTotal)]]
                    } else {
                        //使用伪造数据作为输入
                        prob = discriminatorParams[fakeData[fakeDataIndex]]
                    }

                    //计算loss并更新参数
                    delta := 0.0
                    if j < batchSize {
                        delta = (1 - prob) * learningRate
                        generatorParams[fakeData[fakeDataIndex]] += delta
                    } else {
                        delta = (-prob) * learningRate
                        discriminatorParams[realData[rand.Intn(realTotal)]] -= delta
                        discriminatorParams[fakeData[fakeDataIndex]] += delta
                    }

                    //缓存loss的计算结果
                    key := fmt.Sprintf("loss_%d_%d", i, fakeDataIndex)
                    cached(key, func() interface{} {
                        return ((1-prob)*(1-prob))*learningRate*learningRate + delta*delta
                    })

                    fakeDataIndex++
                }

                wg.Done()
            }()
        }

        wg.Wait()

        //缓存模型参数的计算结果
        for j := range generatorParams {
            key := fmt.Sprintf("generator_%d_%d", i, j)
            cached(key, func() interface{} {
                return generatorParams[j]
            })
        }

        for j := range discriminatorParams {
            key := fmt.Sprintf("discriminator_%d_%d", i, j)
            cached(key, func() interface{} {
                return discriminatorParams[j]
            })
        }

        fmt.Printf("Iteration %d finished.
", i)
    }
}